The landscape of big data analytics is undergoing a fundamental transformation to accommodate the rise of autonomous AI agents. A group of four former Microsoft data infrastructure specialists has officially exited stealth mode to introduce Edelbyte, a specialized analytics platform engineered specifically to meet the rigorous performance requirements of AI-driven workflows.

Founded in 2025 with dual headquarters in Tel Aviv and San Francisco, the startup has successfully secured $70 million in combined seed and Series A financing. The funding round was spearheaded by Sequoia and Quiet Capital, with additional backing from Greenoaks and Swish Ventures.

The founding team—CEO Avner Aharoni, CTO Evgeney Ryzhyk, VP of Engineering Alexander Sloutsky, and VP of Platform Engineering Yoni Leibowitz—brings over a decade of collective experience from Microsoft. Their pedigree includes pivotal roles in developing Azure Kusto (Azure Data Explorer), the industry-standard managed service for real-time, time-series analysis of massive data streams. Edelbyte is already demonstrating significant market traction, supporting petabyte-scale operations for several Fortune 500 clients.

Optimizing Analytics for the Agentic Era

As the big data sector prepares for substantial expansion—with projections from Markets and Markets suggesting the industry will climb from $324.59 billion in 2026 to $516.29 billion by 2031—Edelbyte is positioning itself to disrupt incumbents like Snowflake, Databricks, and IBM.

We are rethinking the data platform to ensure it is perfectly optimized for the age of AI agents, said CEO Avner Aharoni.

Aharoni emphasizes that legacy infrastructure is ill-equipped for the sheer volume and complexity of queries generated by AI agents, which operate at speeds far exceeding human capability. To remain competitive, enterprises require engines that prioritize extreme CPU efficiency and rapid response times.

The AI-First Advantage

Industry analysts, including Andy Neill of Infotech, note that traditional analytics tools often fail to provide the context necessary for Retrieval-Augmented Generation (RAG) architectures. While competitors are attempting to patch legacy systems with add-on features, Edelbyte distinguishes itself by:

  • Utilizing a unified engine built from the ground up for AI-first performance.
  • Providing advanced query federation capabilities.
  • Eliminating the inefficiencies inherent in adapting traditional star-schema reporting for autonomous agents.

By focusing on a native architecture rather than retrofitting existing tools, Edelbyte aims to provide a faster, more cost-effective solution for enterprises looking to empower their AI agents with real-time, actionable data.